{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:54:50Z","timestamp":1777614890119,"version":"3.51.4"},"reference-count":60,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:p>Over the past decade, millions of smart meters have been installed by electricity suppliers worldwide, allowing them to collect a large amount of electricity consumption data, albeit sampled at a low frequency (one point every 30min). One of the important challenges these suppliers face is how to utilize these data to detect the presence\/absence of different appliances in the customers' households. This valuable information can help them provide personalized offers and recommendations to help customers towards the energy transition. Appliance detection can be cast as a time series classification problem. However, the large amount of data combined with the long and variable length of the consumption series pose challenges when training a classifier. In this paper, we propose ADF, a framework that uses subsequences of a client consumption series to detect the presence\/absence of appliances. We also introduce TransApp, a Transformer-based time series classifier that is first pretrained in a self-supervised way to enhance its performance on appliance detection tasks. We test our approach on two real datasets, including a publicly available one. The experimental results with two large real datasets show that the proposed approach outperforms current solutions, including state-of-the-art time series classifiers applied to appliance detection.<\/jats:p>","DOI":"10.14778\/3632093.3632115","type":"journal-article","created":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T11:26:31Z","timestamp":1705749991000},"page":"553-562","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["ADF &amp; TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series"],"prefix":"10.14778","volume":"17","author":[{"given":"Adrien","family":"Petralia","sequence":"first","affiliation":[{"name":"EDF R&amp;D - Universit\u00e9 Paris Cit\u00e9"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philippe","family":"Charpentier","sequence":"additional","affiliation":[{"name":"EDF R&amp;D"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Themis","family":"Palpanas","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris Cit\u00e9 - IUF"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"2009","year":"2012","unstructured":"2012. CER Smart Metering Project - Electricity Customer Behaviour Trial, 2009-2010. https:\/\/www.ucd.ie\/issda\/data\/commissionforenergyregulationcer\/","journal-title":"CER Smart Metering Project - Electricity Customer Behaviour Trial"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2013.2266122"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpubeco.2011.03.003"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2022.112087"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1602.01711"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","unstructured":"Shaojie Bai J. Zico Kolter and Vladlen Koltun. 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. 10.48550\/ARXIV.1803.01271","DOI":"10.48550\/ARXIV.1803.01271"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/EPEC.2015.7379940"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526183"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","unstructured":"Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel M. Ziegler Jeffrey Wu Clemens Winter Christopher Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language Models are Few-Shot Learners. 10.48550\/ARXIV.2005.14165","DOI":"10.48550\/ARXIV.2005.14165"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/SCSP.2016.7501033"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1967.1053964"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","unstructured":"Zihang Dai Hanxiao Liu Quoc V. Le and Mingxing Tan. 2021. CoAtNet: Marrying Convolution and Attention for All Data Sizes. 10.48550\/ARXIV.2106.04803","DOI":"10.48550\/ARXIV.2106.04803"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1810.07758"},{"key":"e_1_2_1_14_1","volume-title":"Webb","author":"Dempster Angus","year":"2019","unstructured":"Angus Dempster, Fran\u00e7ois Petitjean, and Geoffrey I. Webb. 2019. ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. CoRR abs\/1910.13051 (2019). arXiv:1910.13051 http:\/\/arxiv.org\/abs\/1910.13051"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.17775\/CSEEJPES.2020.03450"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1810.04805"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","unstructured":"Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly Jakob Uszkoreit and Neil Houlsby. 2020. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. 10.48550\/ARXIV.2010.11929","DOI":"10.48550\/ARXIV.2010.11929"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119619"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00710-y"},{"key":"e_1_2_1_20_1","unstructured":"Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Doll\u00e1r and Ross Girshick. 2021. Masked Autoencoders Are Scalable Vision Learners. arXiv:2111.06377 [cs.CV]"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Deep Residual Learning for Image Recognition. 10.48550\/ARXIV.1512.03385","DOI":"10.48550\/ARXIV.1512.03385"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","unstructured":"Dan Hendrycks and Kevin Gimpel. 2016. Gaussian Error Linear Units (GELUs). 10.48550\/ARXIV.1606.08415","DOI":"10.48550\/ARXIV.1606.08415"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-013-0322-1"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1502.03167"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00619-1"},{"key":"e_1_2_1_26_1","unstructured":"ISSDA. [n.d.]. Irish Social Science Data Archive. https:\/\/www.ucd.ie\/issda\/"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","unstructured":"Matthias Kahl Daniel Jorde and Hans-Arno Jacobsen. 2022. Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning. 10.48550\/ARXIV.2209.03759","DOI":"10.48550\/ARXIV.2209.03759"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077839.3077845"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22155872"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447555.3464865"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3224044"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","unstructured":"Min Lin Qiang Chen and Shuicheng Yan. 2013. Network In Network. 10.48550\/ARXIV.1312.4400","DOI":"10.48550\/ARXIV.1312.4400"},{"key":"e_1_2_1_33_1","unstructured":"Min Lin Qiang Chen and Shuicheng Yan. 2014. Network In Network. arXiv:1312.4400 [cs.NE]"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2021.08.045"},{"key":"e_1_2_1_35_1","volume-title":"Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony J. Bagnall.","author":"Middlehurst Matthew","year":"2021","unstructured":"Matthew Middlehurst, James Large, Michael Flynn, Jason Lines, Aaron Bostrom, and Anthony J. Bagnall. 2021. HIVE-COTE 2.0: a new meta ensemble for time series classification. CoRR abs\/2104.07551 (2021). arXiv:2104.07551 https:\/\/arxiv.org\/abs\/2104.07551"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISGT.2014.6816507"},{"key":"e_1_2_1_37_1","volume-title":"International Conference on Learning Representations.","author":"Nie Yuqi","year":"2023","unstructured":"Yuqi Nie, Nam H. Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2023. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. In International Conference on Learning Representations."},{"key":"e_1_2_1_38_1","volume-title":"An Introduction to Convolutional Neural Networks. CoRR abs\/1511.08458","author":"O'Shea Keiron","year":"2015","unstructured":"Keiron O'Shea and Ryan Nash. 2015. An Introduction to Convolutional Neural Networks. CoRR abs\/1511.08458 (2015). arXiv:1511.08458 http:\/\/arxiv.org\/abs\/1511.08458"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/WoWMoM.2013.6583496"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1912.01703"},{"key":"e_1_2_1_41_1","unstructured":"Adrien Petralia. [n.d.]. Source code of TransApp experiments. https:\/\/github.com\/adrienpetralia\/TransApp"},{"key":"e_1_2_1_42_1","volume-title":"Appliance Detection Using Very Low-Frequency Smart Meter Time Series. In ACM International Conference on Future Energy Systems (e-Energy).","author":"Petralia Adrien","year":"2023","unstructured":"Adrien Petralia, Philippe Charpentier, Paul Boniol, and Themis Palpanas. 2023. Appliance Detection Using Very Low-Frequency Smart Meter Time Series. In ACM International Conference on Future Energy Systems (e-Energy)."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2022.112749"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.egypro.2017.07.371"},{"key":"e_1_2_1_45_1","volume-title":"Empirical inference","author":"Schapire Robert E","unstructured":"Robert E Schapire. 2013. Explaining adaboost. In Empirical inference. Springer, 37--52."},{"key":"e_1_2_1_46_1","volume-title":"3rd International Conference on Learning Representations (ICLR","author":"Simonyan K","year":"2015","unstructured":"K Simonyan and A Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations (ICLR 2015), 1--14."},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22082926"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.14778\/3611479.3611536"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","unstructured":"Christian Szegedy Wei Liu Yangqing Jia Pierre Sermanet Scott Reed Dragomir Anguelov Dumitru Erhan Vincent Vanhoucke and Andrew Rabinovich. 2014. Going Deeper with Convolutions. 10.48550\/ARXIV.1409.4842","DOI":"10.48550\/ARXIV.1409.4842"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2016.2584581"},{"key":"e_1_2_1_51_1","volume-title":"CoRR abs\/1706.03762","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. CoRR abs\/1706.03762 (2017). arXiv:1706.03762 http:\/\/arxiv.org\/abs\/1706.03762"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.14778\/3570690.3570698"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","unstructured":"Zhiguang Wang Weizhong Yan and Tim Oates. 2016. Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline. 10.48550\/ARXIV.1611.06455","DOI":"10.48550\/ARXIV.1611.06455"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","unstructured":"Qingsong Wen Tian Zhou Chaoli Zhang Weiqi Chen Ziqing Ma Junchi Yan and Liang Sun. 2022. Transformers in Time Series: A Survey. 10.48550\/ARXIV.2202.07125","DOI":"10.48550\/ARXIV.2202.07125"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2106.13008"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3427771.3429390"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","unstructured":"George Zerveas Srideepika Jayaraman Dhaval Patel Anuradha Bhamidipaty and Carsten Eickhoff. 2020. A Transformer-based Framework for Multivariate Time Series Representation Learning. 10.48550\/ARXIV.2010.02803","DOI":"10.48550\/ARXIV.2010.02803"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.114949"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2012.07436"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","unstructured":"Tian Zhou Ziqing Ma Qingsong Wen Xue Wang Liang Sun and Rong Jin. 2022. FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting. 10.48550\/ARXIV.2201.12740","DOI":"10.48550\/ARXIV.2201.12740"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3632093.3632115","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T11:28:46Z","timestamp":1705750126000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3632093.3632115"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11]]},"references-count":60,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["10.14778\/3632093.3632115"],"URL":"https:\/\/doi.org\/10.14778\/3632093.3632115","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2023,11]]},"assertion":[{"value":"2024-01-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}